The natural frequency of the first bending mode of monopile-founded offshore wind turbines (OWTs) is decreasing along with the general trend of increasing turbine size. In addition, it is observed that in some cases the natural frequency starts to approach the rotor 1P frequency, which may trigger an increase in (wind) fatigue loading due to resonance effects. To mitigate this risk there are various options such as adjusting the support structure design by increasing the diameter and/or wall thickness, but this has a direct impact on the Capital Expenditures (CAPEX). Alternatively, enhancing soilstiffness in engineering models allows to minimise the reliance on additional steel in the design. Evaluation of in-field frequency measurements from various offshore wind farms indicates a consistent trend: natural frequencies estimated during the design phase are often lower than the frequencies measured under in-situ conditions. A portion of observed “frequency gap” can be attributed to uncertainties and conservative assumptions in geotechnical design aspects, such as soil interpretation, soil-structure interaction modelling methods, presence of a scour protection system, installation and pile-soil interface ageing effects. Using measurements from several installed OWTs, this study demonstrates how addressing these factors during the design process can help bridge the frequency gap. Furthermore, this paper aims to feed a community-wide discussion on the extent to which the reported findings can be incorporated in the design phase of offshore wind support structures. The overarching goal is to achieve more accurate estimate of the natural frequency, reducing design conservatism, optimising steel usage, and minimising associated project costs.

Operational modal analysis (OMA) is an essential tool for understanding the structural dynamics of offshore wind turbines (OWTs). However, the classical OMA algorithms require the excitation of the structure to be stationary white noise, which is often not the case for operational OWTs due to the presence of periodic excitation caused by rotor rotation. To address this issue, several solutions have been proposed in the literature, including the Kalman filter-based stochastic subspace identification (KF-SSI) method which eliminates harmonics through estimation and orthogonal projection. In this paper, an enhanced version of the KF-SSI method is presented that involves a concatenation step, allowing multiple datasets with similar environmental conditions to be used in the identification process, resulting in higher precision. This enhanced framework is applied to an operational OWT and compared to other OMA methods, such as the modified least-squares complex exponential and PolyMAX. Using field data from a multi-megawatt operational OWT, it is shown that the enhanced framework is able to accurately distinguish the first three bending modes with more stable estimates and lower variance compared to the original KF-SSI algorithm and follows a similar trend compared to other approaches.

Fatigue assessment in offshore wind turbine support structures requires the monitoring of strains below the mudline, where the highest bending moments occur. However, direct measurement of these strains is generally impractical. This paper presents the validation of a virtual sensing technique based on the Gaussian process latent force model for dynamic strain monitoring. The dataset, taken from an operating near-shore turbine in the Westermeerwind Park in the Netherlands, provides a unique opportunity for validation of strain estimates at locations below the mudline using strain gauges embedded within the monopile foundation.

To increase the contribution of offshore wind energy to the global energy mix in an economically sustainable manner, it is required to reduce the costs associated with the production and operation of offshore wind turbines (OWTs). One of the largest uncertainties and sources of conservatism in design and lifetime prediction for OWTs is the determination of the global damping level of the OWT. Estimation of OWT damping based on field measurement data has hence been subject to considerable research attention and is based on the use of (preferably operational) vibration data obtained from sensors mounted on the structure. As such, it is an output-only problem and can be addressed using state-of-the-art operational modal analysis (OMA) techniques, reviewed in this paper. The evolution of classical time- and frequency-domain OMA techniques has been reviewed; however, the literature shows that the OWT vibration data are often contaminated by rotor speed harmonics of significantly high energy located close to structural modes, which impede classical damping identification. Recent advances in OMA algorithms for known or unknown harmonic frequencies can be used to improve identification in such cases. Further, the transmissibility family of OMA algorithms is purported to be insensitive to harmonics. Based on this review, a classification of OMA algorithms is made according to a set of novel suitability criteria, such that the OMA technique appropriate to the specific OWT vibration measurement setup may be selected. Finally, based on this literature review, it has been identified that the most attractive future path for OWT damping estimation lies in the combination of uncertain non-stationary harmonic frequency measurements with statistical harmonic isolation to enhance classical OMA techniques, orthogonal removal of harmonics from measured vibration signals, and in the robustification of transmissibility-based techniques.